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Review

Advances of QTL Localization and GWAS Application in Crop Resistances Against Plant-Parasitic Nematodes

by
Jing-Wen Yu
1,†,
Ling-Wei Wan
2,†,
Huan-Huan Hao
1,
Wen-Cui Wu
1,
Ya-Qin Liu
1,
Xi-Yue Yu
1,
De-Liang Peng
1,
Huan Peng
1,
Shi-Ming Liu
1,
Ling-An Kong
1,
Hou-Xiang Kang
1 and
Wen-Kun Huang
1,*
1
State Key Laboratory for Biology of Plant Diseases and Insect Pests, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, Beijing 100193, China
2
Plant Pathology and Plant-Microbe Biology Section, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Agronomy 2025, 15(10), 2370; https://doi.org/10.3390/agronomy15102370 (registering DOI)
Submission received: 6 August 2025 / Revised: 5 October 2025 / Accepted: 9 October 2025 / Published: 10 October 2025
(This article belongs to the Section Pest and Disease Management)
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Abstract

Plant-parasitic nematodes (PPNs) pose a significant threat to agricultural production and global food security. To mitigate this challenge, quantitative trait locus (QTL) mapping and genome-wide association studies (GWAS) have been extensively employed in crop resistance breeding research. These methods have identified resistance-related genes and genetic markers, offering a solid scientific basis and practical tools for resistance breeding. This review summarizes recent advances in QTL and GWAS applications for enhancing resistance to cyst nematodes (Heterodera glycines, H. filipjevi, and H. avenae), root-knot nematodes (Meloidogyne graminicola and M. incognita), and root-lesion nematodes (Pratylenchus spp.). It also evaluates the commercial deployment of resistance genes, discusses integrated breeding strategies, and highlights future research directions toward developing durable nematode-resistant crops.

1. Introduction

In recent decades, the rapid growth of the global population has posed significant challenges in ensuring food security [1]. Key concerns regarding food include its availability, safety, adequacy, and nutritional quality [2]. Among the threats to crop production, plant-parasitic nematodes (PPNs) present significant challenges to agriculture and global food security [3]. To date, over 4100 species of PPNs have been identified [4]. PPNs have been classified as sedentary or migratory based on their feeding behavior within the root systems [5]. Among the most destructive PPNs are root-knot nematodes (Meloidogyne spp.), cyst nematodes (Heterodera spp.), and root-lesion nematodes (Pratylenchus spp.) [6]. Statistically, it is estimated that PPNs cause annual losses of up to USD 157 billion in global crop yields [7].
Management strategies for PPNs are diverse, typically involving cultural, chemical, and biological methods [8]. Traditional approaches such as crop rotation, cover cropping, and organic amendments are commonly used to suppress nematode populations and improve soil health [9]. Nematode populations are suppressed, and soil health is enhanced through the implementation of crop rotation, cover cropping, soil solarization, and organic amendments [10]. However, challenges such as the complexity of crop selection, high labor costs, and low efficiency limit the effectiveness of these methods. Although chemical nematicides have traditionally been considered the most effective means of control, their long-term use can lead to environmental contamination, resistance development, and health risks [11,12]. In contrast, biological control has been recognized as an eco-friendly and sustainable management strategy for PPNs. This approach involves the utilization of natural enemies, such as fungi, bacteria, and other organisms, to regulate nematode populations [10]. However, the effectiveness of biological control is influenced by environmental conditions and soil microbial competition, and challenges related to consistency and cost have been observed in large-scale applications [13]. Recently, increasing attention has been directed toward the development of alternative and integrated management strategies for PPN control.
Breeding-resistant varieties are frequently considered one of the most effective measures for controlling PPNs [14]. Traditional breeding, genetic engineering, and marker-assisted selection (MAS) are the three main technological approaches currently used to breed nematode-resistant crops [15]. Traditional breeding involves selecting and crossing plants with desirable traits. Although effective over the long term, this method is time-consuming and not suitable for rapid development [16]. In genetic engineering, resistance is conferred to crops through the introduction of exogenous genes into the crop genome [17]. Despite its significant advantage in accelerating the breeding process, concerns regarding potential safety and environmental impacts have led to widespread controversy, thereby limiting its broad application in agricultural production [18]. MAS, a modern molecular marker-based breeding technology, enables the rapid identification and screening of nematode resistance traits at an early stage of breeding, thereby enhancing breeding efficiency [19].
The complexity of most resistance-related crop traits stems from the influence of multiple genetic loci, environmental factors, and their interactions [20]. Genetic insights into these complex traits have increasingly been gained through quantitative trait loci (QTL) mapping and genome-wide association study (GWAS), which have become pivotal in advancing crop breeding efforts [21]. QTL mapping has been utilized to identify genome regions linked to phenotypic traits through linkage mapping in biparental populations [22]. In contrast, GWAS has been applied to rapidly detect genetic variants associated with complex traits by analyzing genotype-phenotype associations within natural populations [23]. QTL mapping and GWAS have been applied to resistance breeding research across multiple crops, encompassing major food crops such as soybean, wheat, and rice, as well as numerous other food and cash crop systems. These approaches have identified numerous resistance-associated genes and genetic markers, providing a robust scientific foundation and practical tools for resistance breeding [21]. In this review, we summarize recent advances in the application of QTL mapping and GWAS for resistance to cyst nematodes in soybean, barley and wheat, root-knot nematodes in rice and tomato, and root-lesion nematodes in wheat. We further discuss emerging research directions, including the breeding of nematode-resistant varieties and genome editing approaches, with the aim of providing new strategies and insights for elucidating the genetic mechanisms underlying crop nematode resistance and for advancing resistance breeding.

2. Comparing QTL Mapping and GWAS in PPNs Resistance

2.1. QTL Mapping in PPNs Resistance: Methodology and Constraints

QTL mapping has been applied to dissect the genetic basis of resistance against PPNs. This approach effectively identifies trait-associated genome regions within a specific population, particularly for traits governed by major effect loci or in hybrid populations with known parental lines [24]. Through QTL localization, researchers can identify candidate genes responsible for complex traits, providing valuable resources for developing molecular markers and thereby accelerating the breeding process [21]. In crops such as soybean, rice, wheat, barley, and tomato, QTL mapping has facilitated the identification of genomic regions associated with resistance to cyst nematodes, root-knot nematodes, and root-lesion nematodes (Table 1).
Nevertheless, QTL mapping faces several limitations in breeding crops for resistance to PPNs. The approach is time-consuming and generally offers low resolution due to limited recombination events, often resulting in broad genomic intervals that are challenging to implement directly in marker-assisted selection (Figure 1). In addition, its reliance on allelic variation between two parental lines restricts the detection of resistance alleles present in diverse germplasm. Consequently, although QTL mapping has identified major resistance loci such as Rhg1 and Mi-1, it remains less effective at capturing the polygenic and environmentally influenced nature of PPNs resistance across varied genetic backgrounds [25,46]. Advances in genome sequencing and the development of high-density molecular markers have partially mitigated these limitations, enabling finer mapping of resistance loci and providing more precise tools to support breeding programs.

2.2. GWAS in PPNs Resistance: Opportunities and Challenges

GWAS is a potent tool for identifying single-nucleotide polymorphisms (SNPs) associated with complex traits [21]. In contrast to QTL mapping, which is confined to bi-parental populations, GWAS can identify numerous SNPs associated with nematode resistance across diverse germplasm panels (Figure 1). For instance, GWAS has revealed resistance loci linked to cyst nematodes and root-knot nematodes, providing novel candidate genes and pathways for functional validation [19] (Table 1). These discoveries expand the pool of resistance sources for breeding and provide insights into the polygenic and complex nature of PPNs resistance.
However, GWAS also faces several challenges. Reliable identification of resistance loci requires large sample sizes, accurate phenotyping under nematode-infested conditions, and high-density genome-wide SNP coverage. Moreover, population structure and genetic heterogeneity can produce false-positive associations, necessitating rigorous statistical control. Despite these limitations, GWAS has proven to be a powerful approach for detecting resistance loci across diverse germplasm, complementing QTL mapping and enabling the integration of both methods to accelerate the development of PPNs-resistant cultivars (Figure 2).

3. QTL Mapping for Crop Resistance to PPNs

3.1. QTL Mapping for Soybean Cyst Nematode

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe, 1952) represents a serious global threat to soybean production, causing annual losses of at least USD 150 billion in the United States [47]. Soybean resistance to SCN is a complex quantitative trait controlled by multiple loci and genes [47]. The Rhg1 (resistance to H. glycines 1) loci was first localized on chromosome 18 using QTL analysis and has been identified in most resistance sources used for breeding commercial soybean varieties, including Peking and PI437654 [48]. Allelic variations of rhg1 exist among different soybean genotypes, with the PI88788 variant designated as rhg1-b. Matsye et al. [49] investigated gene expression within the 67 kb genomic region of the rhg1-b locus. Three genes, Glyma.18G022400 (encoding an amino acid transporter), Glyma.18G022500 (GmSNAP18, encoding α-SNAP protein), and Glyma.18G022700 (encoding a WI12 protein) were identified to mediate SCN resistance [25,50]. Notably, the amino acid transporter (Glyma18g02580) and α-SNAP (Glyma18g02590) showed specific expression in syncytia during SCN defense in both Peking (PI548402) and PI88788 genotypes. The WI12 protein may participate in producing phenazine-like compounds with potential nematotoxic effects. And the α-SNAP protein, involved in vesicle trafficking, influences food exocytosis in syncytia and consequently affects nematode physiology. The plant transporter Glyma18g02580 contains a tryptophan/tyrosine permease family domain. Tryptophan catabolism yields indole-3-acetic acid, a precursor of the phytohormone auxin [51]. The second major effector QTL conferring SCN resistance was subsequently identified and localized to the Rhg4 locus (LGA2) on Chr8, which has also been detected in several known resistance sources such as Peking and PI437654 [52,53,54]. The SHMT gene (GmSHMT08) was confirmed as the resistance gene at the Rhg4 locus [26]. This enzyme catalyzes the transfer of a methylene carbon from glycine to tetrahydrofolate (THF), generating methylene–THF. Subsequently, methylene–THF reacts with a second glycine molecule to form L-serine in the glycolate pathway [55]. This reaction produces S-adenosylmethionine (SAM), a precursor for both polyamines and the plant hormone ethylene [56]. Notably, the enzymatic properties of SHMT are altered in the resistance allele due to two amino acid substitutions (P130R and N385Y) in GmSHMT08. These modifications disrupt folate homeostasis in syncytia, ultimately triggering a hypersensitive response (HR) that leads to programmed cell death (PCD) [57]. Importantly, the GmSHMT08 allele differs significantly between resistant and susceptible plants [26]. In summary, the functional characterization of genes such as GmSNAP18, GmSHMT08, GmSNAP11, and GmSNAP02, identified through QTL mapping, has led to substantive progress in elucidating the molecular mechanisms of resistance to SCN. The MTAs or QTLs used in breeding for PPNs resistance are shown in Table 1, and the genes identified against PPNs are shown in Table 2.

3.2. QTL Mapping for Cereal Cyst Nematode and Root-Lesion Nematode

The cereal cyst nematode (CCN, Heterodera avenae Wollenweber, 1924) and root-lesion nematode (RLN, Pratylenchus spp.) are recognized as PPNs that cause severe damage to wheat and other cereal crops, such as barley and oats [78]. The development of CCN/RLN-resistant cereal varieties through QTL has been established as an important objective in ongoing breeding programs [79,80]. Few QTL associated with CCN/RLN resistance have been identified in wheat (Table 1). Researchers have reported the transfer of nine resistance genes from wild relatives (such as Egyptian wheat) into common wheat to enhance CCN resistance [58]. These genes include Cre1 and Cre8 from wheat; Cre3 and Cre4 from Ae. tauschii Coss. Ae. ventricosa (Zhuk.); Cre2, Cre5, and Cre6; and Cre7 from Ae. triuncialis L [59]. The primary QTL Cre8 was localized near the distal end of chromosome arm 6BL [60,81]. Similarly, in hexaploid wheat, many resistance genes have been introgressed from its progenitors. The Cre1 gene has been reported to be highly effective against H. avenae populations in North Africa, Europe, and North America [82]. Cre3 has been found to be effective against H. avenae in Australia [83]. Broad-spectrum resistance against multiple Heterodera species and pathotypes has been associated with Cre2 and Cre4 genes from Aegilops spp., as well as an unidentified gene from the wheat line AUS4930 [84]. These resistance loci (Cre1 to Cre7) have been established in wheat worldwide by the International Maize and Wheat Improvement Center (CIMMYT), conferring substantial resistance against CCNs. In summary, nine genes conferring resistance to CCN (Cre1-Cre8, Cre8V) have been characterized, while CreV, CreY, and CreR represent more recent discoveries. By contrast, Rlnn1 remains the sole gene reported for RLN resistance in wheat. Despite the limited genetic diversity of known resistance sources, advances in genomics and the utilization of global wheat germplasm, including wild relatives, provide promising prospects for developing durable resistance to CCN and RLN.

3.3. QTL Mapping for Rice Root-Knot Nematode

Rice root-knot nematode (RRKN, Meloidogyne graminicola Golden & Birchfield, 1965) is a significant pest affecting rice production globally. In 1999, RRKN-resistant resources were initially screened in African wild rice and African cultivated rice, including the long-stamen wild rice WL02-2 and WL02-15, as well as the African cultivated rice TOG7235, TOG5674, and TOG5675 [85]. Subsequently, recombinant inbred lines (RILs) derived from several hybrid combinations of African and Asian cultivated rice were utilized to identify RRKN-resistant lines. Among a total of 122 RILs, three QTLs loci were identified based on root-knot number and nematode counts, which served as resistance evaluation indices [86]. In 2007, Shrestha et al. [87] used RILs of two Asian rice varieties, Bala/Azucena, as experimental materials, and obtained for the first time six QTLs loci for resistance to RRKN through resistance phenotyping. Additionally, three resistance QTLs loci were identified from a population of RILs derived from a cross combination of RRKN-resistant Indian cultivated rice ‘TKM6’ and the susceptible cultivar Annapurna [88]. Recently, a hybrid population derived from the resistant varieties HKG 98 and Zhonghua 11 was genetically analyzed, and a master RKN resistance gene, MG1, was identified. Mapping-based cloning and functional analyses demonstrated that MG1 encodes a coiled-coil, nucleotide-binding, and leucine-rich repeat (CC-NB-LRR) protein. Loss-of-function mutations specifically within the LRR domain resulted in a complete loss of resistance, establishing that this domain is indispensable for nematode-resistance [61]. Compared with the QTL mapping of resistance to CCN and SCN, few resistance genes have been identified for RRKN. Only MG1 was identified to confer resistance to RRKN, underscoring the need for further exploration of genetic resources and novel resistance mechanisms.

3.4. QTL Mapping for Southern Root-Knot Nematode

Resistance to Southern root-knot nematode (SRKN, Meloidogyne incognita (Kofoid & White, 1919) Chitwood, 1949) is described as a complex trait, and biparental populations have historically been used for QTL identification [89,90]. Mi-1, originally identified in tomato, was the first gene conferring resistance to both SRKN and potato aphids (Macrosiphum euphorbiae (Thomas, 1878)) and has been widely utilized in commercial varieties [91]. To date, three additional Mi (Mi-3, Mi-5, and Mi-9) resistance genes have been mapped and shown to confer resistance to SRKN in wild tomato [92]. The Mi gene encodes a canonical leucine-rich repeat nucleotide binding site (LRR-NBS) architecture. The central region of Mi contains a highly conserved 260-amino acid NBS domain, characteristic of this resistance gene family [93]. The C-terminal region comprises 14 imperfect LRR motifs, each averaging 24 amino acids [94]. Structural analysis reveals the NBS domain displays significant conformational complexity, consistent with its established role in defense signal transduction. Current evidence strongly supports the LRR domain as the primary determinant of pathogen recognition specificity, a functional characteristic shared among numerous plant resistance proteins. The major QTL on chromosome 10 and the minor QTL on chromosome 18 of soybean, both conferring resistance to SRKN, have been reported to be derived from exotic germplasm (PI96354) [95]. A QTL mapping of 246 RILs originating from Magellan × PI438489B was utilized, where a major QTL was identified on chromosome 10 of soybean, near a previously described region, and two other minor QTLs were identified on chromosomes 8 and 13 [96]. In another study, fifty-three QTLs associated with SRKN resistance were identified, some of which showed stability across different environments, an important factor for breeding resistance in multiple environments [97]. However, resistance conferred by Mi genes is often overcome by high temperatures and SRKN variability, highlighting the need to validate novel QTLs and identify major-effect genes for broader, durable resistance.

4. Application of GWAS for Crop Resistance to PPNs

4.1. Application of GWAS for Crop Resistance to Soybean Cyst Nematode

The substantial natural variation observed among different soybean varieties has provided a foundation for the application of GWAS. For instance, Wen et al. [98] genotyped 363 accessions, with association mapping revealing resistance-linked markers and a homozygous gene cluster at the soybean Rhg1 locus. Through the GWAS approach, new resistance loci distinct from existing SCN resistance genes have been identified. Zhang et al. [99] identified multiple SNP loci significantly associated with SCN resistance through GWAS analysis, three of which were localized in previous QTL intervals, including Rhg1 and Rhg4. GWAS results also identified 10 SNPs associated with SCN resistance in five different genome regions, and these loci provide important genetic resources for future resistance breeding. Moreover, GWAS of 481 re-sequenced soybean accessions identified 23 regions conferring SCN race 3 resistance, with ten overlapping IBD-detected loci [100]. Haplotype analysis pinpointed a causative SNP in the promoter of Glyma.08G096500, encoding a TIFY5b-related protein on chromosome 8, strongly associated with resistance. Compared with QTL mapping, GWAS has advanced the identification of novel genetic loci beyond the major-effect QTLs Rhg1 and Rhg4, providing valuable molecular markers for breeding. Nevertheless, many associated SNPs account for only minor phenotypic variation and require functional validation. In addition, population structure and allelic heterogeneity often limit the reproducibility of GWAS-identified genes across diverse genetic backgrounds, constraining their direct application in marker-assisted selection.

4.2. Application of GWAS for Crop Resistance to Cereal Cyst Nematode

In recent years, GWAS have been increasingly applied to investigate CCN resistance in wheat. Pariyar et al. [101] performed GWAS for the first time on 161 winter wheat materials using 90K iSelect SNP microarrays, and 11 resistance-related QTLs were identified. Among these, the DNA methyltransferase 1-associated protein 1 (DMAP1) gene and a putative RING/FYVE/PHD-type zinc finger gene were found to participate in programmed cell death and resistance responses against pests. According to the authors, this study represents the first GWAS-based mapping of wheat resistance to CCNs. Likewise, A subsequent GWAS analysis identified 11 significant marker-trait associations (MTAs) strongly linked to CCN resistance in wheat, with some markers co-localized with known resistance genes, highlighting the genetic diversity of CCN resistance [102]. A total of 33 putative candidate genes were identified for the ten significant MTAs associated with H. avenae resistance. Among these, only 17 candidate genes, including those encoding F-box-like domain superfamily proteins, ankyrin repeat-containing domains proteins, wall-associated receptor kinase galacturonan-binding proteins, coiled-coil domains proteins, serine/threonine kinases proteins, WD40 repeats proteins, and Zinc finger RING/FYVE/PHD-type domains proteins, were predicted to play potential roles in disease resistance. Similarly, Vikas et al. [103] screened 141 Indian wheat genotypes and identified 33 genes like F-box-like domain superfamily, Cytochrome P450 superfamily, Leucine-rich repeat, cysteine-containing subtype Zinc finger RING/FYVE/PHD-type, etc., having a putative role in CCN resistance. Recently, a study utilizing 152K SNP microarrays to perform GWAS on 188 wheat accessions identified 11 resistance markers [104]. Collectively, GWAS have uncovered numerous marker-trait associations for wheat CCN resistance, but genome complexity, population structure, and environmental effects limit reproducibility and necessitate further validation before application in breeding.

4.3. Application of GWAS for Crop Resistance to Rice Root-Knot Nematode

GWAS was conducted on 272 wild rice germplasm, leading to the identification of 17 novel SNP loci associated with resistance [105]. Forty resistant accessions were identified, with 17 novel SNPs significantly associated with resistance traits (gall number, egg masses, eggs/egg mass, and MF). These SNP loci fall within quantitative trait regions harboring candidate genes that encode NBS-LRR proteins, Cf2/Cf5 resistance proteins, and transcription factors including MYB, bZIP, ARF, SCARECROW, and WRKY [105]. Furthermore, Dimkpa et al. [106] evaluated 332 rice varieties for resistance and susceptibility using the root galls index as an evaluation criterion. The resistant varieties Khao Pahk Maw and LD 24 were identified through GWAS, and the study identifies several candidate genes deserving of further study, with particular emphasis on genes containing lectin domains and those located on chromosome 11 that show homology to barley’s Mla resistance locus [106]. Overall, GWAS for RRKN have substantially expanded the genetic resource pool for resistance by identifying multiple novel loci and candidate genes.

4.4. Application of GWAS for Crop Resistance to Southern Root-Knot Nematode

Significant advancements have been made in recent years in the identification of resistance genes and GWAS for SRKN. Complex genetic structures associated with SRKN resistance were revealed through GWAS conducted on common soybean, which has provided an important reference for breeding programs [45]. Additionally, SNPs located on chromosomes Pv06, Pv07, Pv08, and Pv11 of four-season soybean were found to be correlated with egg mass number, while root wear was associated with SNPs on chromosomes Pv01, Pv02, Pv05, and Pv10 [45]. Similarly, GWAS analysis of SRKN phenotypes in 193 soybean varieties was performed [107]. Among these, three most significant SNPs were all localized within Glyma10g017100, which encodes a bifunctional protein containing two distinct structural domains: a pectin esterase (PEC)-like domain and a pectin methyl esterase inhibitor (PMI) domain. Notably, these resistance loci were all localized within a small (3.4 kb) region of chromosome 10. These GWAS have elucidated the complex genetic architecture of SRKN resistance, identifying both novel genomic regions and candidate genes with putative biological functions.

5. Applications and Future Perspectives

5.1. Application of Resistance Genes in Breeding Programs

The development of soybean varieties resistant to SCN is considered an economical and effective control strategy. The Rhg1 locus, the first major resistance locus identified from the resistant variety PI88788, is located on chromosome 18 of soybean. Resistance in more than 95% of SCN-resistant varieties in the United States (totaling 810 varieties) is derived from the Rhg1 locus in the PI88788 strain, highlighting the stability and utility of this locus [108]. Through chromosomal fluorescence in situ hybridization (FISH), Cook et al. [25] demonstrated that Rhg1 is a 31.2 kb gene fragment, and its multicopy nature is directly associated with SCN resistance. Specifically, susceptible varieties, such as Williams 82, were identified as Rhg1 single-copy type (rhg1-c), while resistant varieties PI548402/Peking were classified as Rhg1 low-copy type (rhg1-a), and PI88788 was characterized as Rhg1 multi-copy type (rhg1-b) [109]. Similarly, the primary dominant QTL Rhg4, derived from Peking and PI437654, was found to undergo multicopy amplification, with a tandem repeat sequence of approximately 35.7 kb in length, leading to the upregulation of gene transcript levels [110]. The haplotype variants of Rhg4 were categorized into the wild-type Rhg4-b and the resistant type Rhg4-a, which are recognized as two resistance loci conferring SCN resistance in soybean [110]. The success of Rhg1 and Rhg4, mediated by copy number variation (CNV), highlights the potential of natural genetic variation for SCN resistance in soybean.
Compared with SCN-resistance breeding, CCN resistance genes from wheat relatives have been introduced into cultivated wheat through distant hybridization and chromosome engineering, via gene introgression or transfer [80]. This approach has led to the creation of numerous resistant materials and the successful development of several new CCN-resistant lines. For instance, the Cre3 and Cre4 genes from T. tauschii were introduced into common cultivated wheat, resulting in the development of a wheat variety exhibiting high resistance to the Ha13 pathotype [111]. Additionally, the Cre2 gene from A. ventricosa was transferred into common wheat using the stepping-stone method, yielding genetically stable lines with high resistance to the Ha71 pathotype in Spain and the Ha11 pathotype in the UK [65]. Furthermore, by crossing Madsen × Liangxing 99, a new line resistant to both powdery mildew and CCN, with an optimal growth period, was developed [37]. Distant hybridization of wheat with wild relatives diversifies CCN resistance, enabling gene pyramiding for more durable and broad-spectrum protection against evolving pathogen populations.
MG1, the first resistance gene against RRKN, was cloned and characterized from the resistant rice variety ZhongHua11 [61]. Studies demonstrated that the introduction of MG1 into susceptible rice varieties conferred resistance levels comparable to those of resistant varieties, highlighting the critical role of MG1 in RRKN resistance. To advance resistance breeding, researchers developed a molecular marker, WXM1, which is tightly linked to MG1. This marker enables efficient screening of rice varieties containing the MG1 gene, providing a valuable molecular tool for disease resistance breeding. Cloning of MG1 marks a breakthrough in rice RRKN resistance, but durable protection likely requires pyramiding with minor-effect QTLs to build a resilient genetic barrier.
In tomato breeding, several disease-resistant varieties controlled by the Mi gene have been developed through crossbreeding [72]. Advances in molecular biology will broaden the application of Mi gene cloning in the development of resistant varieties Messeguer et al. [112] constructed a high-resolution RFLP map of the Mi gene flanking region, and Aarts developed an acid phosphatase-1 (APS-1) probe tightly linked to Mi gene. These achievements have provided technical support for the precise integration of the Mi gene into tomato varieties. Overall, the successful introgression of the Mi gene into commercial tomato varieties represents a landmark achievement in breeding for nematode resistance.

5.2. New Strategies in Breeding Crops for Resistance to PPNs

With the refinement of genome editing technologies, researchers have new ways to precisely edit resistance genes and validate their functions, opening up novel strategies for resistance breeding [113]. By combining gene editing technologies, especially cutting-edge technologies like CRISPR, with high-throughput screening (HTP) and predictive modeling using artificial intelligence (AI), the application of existing genome technologies can be dramatically enhanced [114,115]. These innovations promise to improve crop resilience and productivity by precisely modifying genetic material and predicting traits more accurately.
The new strategy consists of four components: (1) in phenotype collection, a multi-scale phenome database is developed through high-throughput analysis, focusing on root topology (e.g., root galls, entrapment angle), leaf physiology, and disease progression metrics (e.g., infestation and lesion expansion rates); (2) for AI algorithm development, an enhanced Mask R-CNN (accuracy > 95%) is used for image segmentation, while a Transformer-based multimodal fusion model integrates phenomic, genomic (SNP chip), transcriptomic (RNA-seq), and metabolomic (LC-MS) data [116]; (3) in genetic mechanism analysis, the investigation of NLR genes and key components of the salicylic acid/jasmonic acid signaling pathway is guided by a workflow comprising GWAS mapping, eQTL analysis, haplotype mining, and gene editing validation, and AlphaFold3 predicted protein interaction networks support CRISPR target design (sgRNA efficiency > 85%) [117]; (4) for field applications, multispectral UAVs with edge computing and Long Range-based Internet of Things (LoRa-based IoT) networks enable real-time monitoring [118]. Smartphone-based diagnostic tools and a standardized cloud database platform support the integration of gene discovery and field deployment, offering a comprehensive solution for nematode resistance breeding in crops (Figure 3).

6. Conclusions and Perspectives

In crop breeding strategies, the identification of gene loci or potential candidate genes is considered for optimizing traits. Pinpointing gene loci and cloning QTLs have helped to reveal the functional basis of plant phenotypes over the last decade. Additionally, GWAS has successfully identified thousands of genetic loci associated with agronomic traits and other characteristics of crops, and multiple methods have been developed to improve resolving power and computational efficiency. By integrating identified QTL with GWAS results, PPNs resistance research can be more effectively advanced, thus achieving more efficient resistance breeding goals. Future work should aim to integrate transcriptomic, proteomic, and metabolomic data to comprehensively analyze plant defense mechanisms against parasitic nematodes. By comparing gene expression patterns under different environmental conditions, researchers can screen for more potent resistance markers and candidate genes. The continued advancement of genomic technologies, combined with interdisciplinary approaches integrating multiple omics platforms, will undoubtedly accelerate the development of crop varieties with enhanced resistance to plant-parasitic nematodes. These developments will contribute significantly to sustainable agricultural practices and global food security in the face of increasing population pressure and environmental challenges.

Author Contributions

Conceptualization, J.-W.Y., L.-W.W., and W.-K.H.; Writing—original draft, J.-W.Y. and L.-W.W.; writing—review and editing, J.-W.Y., L.-W.W., and W.-K.H.; investigation, H.-H.H. and W.-C.W.; methodology, H.-H.H. and W.-C.W.; formal analysis, H.-H.H. and W.-C.W.; data curation, Y.-Q.L. and X.-Y.Y.; validation, Y.-Q.L. and X.-Y.Y.; software, Y.-Q.L., X.-Y.Y., and H.-X.K.; supervision, D.-L.P., H.P., and L.-A.K.; visualization, D.-L.P., H.P., and S.-M.L.; resources, D.-L.P., H.P., S.-M.L., and L.-A.K.; project administration, S.-M.L. and L.-A.K.; funding acquisition, H.-X.K. and W.-K.H. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by National Key Research and Development Program (2024YFC2607600), and The Agricultural Science and Technology Innovation Program (ASTIP) (CAAS-ZDRW202505).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare that they have no conflict of interest.

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Figure 1. Comparison of quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) analysis in plant-parasitic nematodes resistance.
Figure 1. Comparison of quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) analysis in plant-parasitic nematodes resistance.
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Figure 2. The principles of genome-wide association study (GWAS) analysis versus quantitative trait loci (QTL) mapping for evaluating resistance to plant-parasitic nematodes. Briefly, GWAS is conducted on natural populations, where genome-wide SNPs data are acquired through high-throughput genotyping technologies (e.g., chips or sequencing). By integrating phenotypic information, statistical models (e.g., linear mixed models) are employed to identify SNPs loci significantly associated with traits. In contrast, QTL mapping is performed using populations with known pedigrees, such as F2 or backcross populations. Genotype data are obtained through molecular markers (e.g., SNPs, SSRs) and are combined with phenotypic data to localize QTL regions associated with quantitative traits on chromosomes using statistical methods (e.g., interval mapping).
Figure 2. The principles of genome-wide association study (GWAS) analysis versus quantitative trait loci (QTL) mapping for evaluating resistance to plant-parasitic nematodes. Briefly, GWAS is conducted on natural populations, where genome-wide SNPs data are acquired through high-throughput genotyping technologies (e.g., chips or sequencing). By integrating phenotypic information, statistical models (e.g., linear mixed models) are employed to identify SNPs loci significantly associated with traits. In contrast, QTL mapping is performed using populations with known pedigrees, such as F2 or backcross populations. Genotype data are obtained through molecular markers (e.g., SNPs, SSRs) and are combined with phenotypic data to localize QTL regions associated with quantitative traits on chromosomes using statistical methods (e.g., interval mapping).
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Figure 3. Workflow for breeding crops resistant to plant-parasitic nematodes (PPNs). This flowchart outlines an integrated strategy combining functional genomics and smart breeding. The process begins with selection of genetic materials, followed by high-throughput phenotyping and genotyping. Data integration supports quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) to pinpoint candidate genes, which are validated through multi-omics tools (RNA sequencing, metabolomics, proteomics) and resistance mechanism studies. AI-based predictive models then guide gene-editing strategies, tested using CRISPR. Field deployment applies smart breeding practices enhanced with drone monitoring and IoT sensors, while cloud platforms support data-driven decision-making. The pipeline concludes with the release of improved PPN-resistant cultivars.
Figure 3. Workflow for breeding crops resistant to plant-parasitic nematodes (PPNs). This flowchart outlines an integrated strategy combining functional genomics and smart breeding. The process begins with selection of genetic materials, followed by high-throughput phenotyping and genotyping. Data integration supports quantitative trait loci (QTL) mapping and genome-wide association study (GWAS) to pinpoint candidate genes, which are validated through multi-omics tools (RNA sequencing, metabolomics, proteomics) and resistance mechanism studies. AI-based predictive models then guide gene-editing strategies, tested using CRISPR. Field deployment applies smart breeding practices enhanced with drone monitoring and IoT sensors, while cloud platforms support data-driven decision-making. The pipeline concludes with the release of improved PPN-resistant cultivars.
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Table 1. Marker-trait association/quantitative trait loci (MTAs/QTLs) identified against plant-parasitic nematode (PPNs) using genome-wide association study (GWAS) and QTL mapping.
Table 1. Marker-trait association/quantitative trait loci (MTAs/QTLs) identified against plant-parasitic nematode (PPNs) using genome-wide association study (GWAS) and QTL mapping.
MTAs/QTLsChromosome Plant SpeciesNematode SpeciesLocalization TechnologyRef.
Soybean cyst nematode (SCN)
Rhg118Glycine max (L.) Merr. Heterodera glycines Ichinohe, 1952 QTL mapping[25]
Rhg48G. maxH. glycinesQTL mapping[26]
Rhg211G. maxH. glycinesQTL mapping[27]
Rhg314G. maxH. glycinesQTL mapping[28]
cqSCN-00316G. maxH. glycinesQTL mapping[29]
cqSCN-00517G. maxH. glycinesQTL mapping[30]
cqSCN-00615G. maxH. glycinesQTL mapping[31]
cqSCN-00718G. maxH. glycinesQTL mapping[31]
cqSCN 1010G. maxH. glycinesQTL mapping[32]
cqSCN1111G. maxH. glycinesQTL mapping[33]
Cereal cyst nematode (CCN)
Ha15DHordeum vulgare L.Heterodera avenae Wollenweber, 1924QTL mapping[34]
Ha25DH. vulgareH. avenaeQTL mapping[34]
Ha31BH. vulgareH. avenaeQTL mapping[35]
Ha47DH. vulgareH. avenaeQTL mapping[35]
Rha21RH. vulgareH. avenaeQTL mapping[36]
QCre-ma7D7DTriticum aestivum L.Heterodera filipjevi (Madzhidov, 1981)
Stelter, 1984 		QTL mapping[37]
QCre-ma2A2AST. aestivumH. filipjeviQTL mapping[37]
Q.Cyst.TZARI.1A1AT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.2A2AT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.1B2BT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.2D2DT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.3A3AT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.6B6BT. aestivumH. filipjeviGWAS[38]
Q.Cyst.TZARI.6D6DT. aestivumH. filipjeviGWAS[38]
Root-lesion nematodes (RLN)
QRlnt.lrc6DST. aestivumPratylenchus spp.QTL mapping[39]
QRlnt.sk-2B2BT. aestivumPratylenchus spp.QTL mapping[40]
QRlnt.sk-6D6DT. aestivumPratylenchus spp.QTL mapping[40]
Rice root-knot nematode (RRKN)
Mg1(t)10Oryza sativa L. Meloidogyne graminicola Golden & Birchfield, 1965QTL mapping[41]
qMGR4.14O. sativaM. graminicolaQTL mapping[42]
qMGR7.17O. sativaM. graminicolaQTL mapping[42]
qMGR9.19O. sativaM. graminicolaQTL mapping[42]
qGR4.14O. sativaM. graminicolaQTL mapping[42]
qGR8.18O. sativaM. graminicolaQTL mapping[42]
qYR5.15O. sativaM. graminicolaQTL mapping[42]
qYR11.111O. sativaM. graminicolaQTL mapping[42]
qJ2RS2.12O. sativaM. graminicolaQTL mapping[42]
qJ2RS3.13O. sativaM. graminicolaQTL mapping[42]
qGR3.13O. sativaM. graminicolaQTL mapping[42]
qGR5.15O. sativaM. graminicolaQTL mapping[42]
qMGR11.111O. sativaM. graminicolaQTL mapping[43]
Southern root-knot nematode (SRKN)
qMi-C1111Gossypium hirsutum L.Meloidogyne incognita (Kofoid & White, 1919) Chitwood, 1949QTL mapping[44]
S1_257146517Pv06Phaseolus vulgaris L.M. incognitaGWAS[45]
S1_320960286Pv07P. vulgarisM. incognitaGWAS[45]
S1_380326043Pv08P. vulgarisM. incognitaGWAS[45]
S1_510326192Pv11P. vulgarisM. incognitaGWAS[45]
S1_46835797Pv01P. vulgarisM. incognitaGWAS[45]
S1_98931885Pv02P. vulgarisM. incognitaGWAS[45]
S1_234697928Pv05P. vulgarisM. incognitaGWAS[45]
S1_425572688Pv10P. vulgarisM. incognitaGWAS[45]
Table 2. Genes identified against plant-parasitic nematodes (PPNs).
Table 2. Genes identified against plant-parasitic nematodes (PPNs).
GeneChromosome Plant SpeciesNematode SpeciesRef.
GmSHMT088Glycine max (L.) Merr. Heterodera glycines Ichinohe, 1952 [26]
CreY3S VAegilops variabilis Eig.Heterodera avenae Wollenweber, 1924[58]
Cre8V2VA. variabilisH. avenae[58]
CreV6VHaynaldia villosa (L.) SchurHeterodera filipjevi (Madzhidov, 1981)
Stelter, 1984 		[59]
Cre65N VAegilops ventricosa TauschH. avenae[59]
Cre12BLTriticum aestivum L. H. avenae[60]
Cre86BLT. aestivumH. avenae[60]
MG111Oryza sativa L. Meloidogyne graminicola Golden & Birchfield, 1965[61]
GmSNAP1818G. maxH. glycines[62]
GmSNAP1111G. maxH. glycines[63]
GmSNAP022G. maxH. glycines[64]
Cre26M VA. ventricosaH. avenae[65]
Cre32DLTriticum tauschii
(Coss.) Schmalh.		H. avenae[66]
Cre42DT. tauschiiH. avenae[67]
Cre52ASA. ventricosaH. avenae[68]
Cre72DLA. ventricosaH. avenae[69]
CreR6RLSecale cereale L.H. avenae[70]
Rlnn17ALT. aestivumPratylenchus spp.[71]
Mi-16Solanum lycopersicum L.Meloidogyne incognita
(Kofoid & White, 1919) Chitwood, 1949 		[72]
Mi-96S. lycopersicumM. incognita[73]
Mi-312S. lycopersicumM. incognita[74]
Mi-56S. lycopersicumM. incognita[74]
Me1P9Piper nigrum L.M. incognita[75]
Vat5Cucumis melo L.M. incognita[76]
Me39Capsicum annuum L.M. incognita[77]
Notes: sup V is used to represent chromosome 5 in Aegilops variabilis.
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Yu, J.-W.; Wan, L.-W.; Hao, H.-H.; Wu, W.-C.; Liu, Y.-Q.; Yu, X.-Y.; Peng, D.-L.; Peng, H.; Liu, S.-M.; Kong, L.-A.; et al. Advances of QTL Localization and GWAS Application in Crop Resistances Against Plant-Parasitic Nematodes. Agronomy 2025, 15, 2370. https://doi.org/10.3390/agronomy15102370

AMA Style

Yu J-W, Wan L-W, Hao H-H, Wu W-C, Liu Y-Q, Yu X-Y, Peng D-L, Peng H, Liu S-M, Kong L-A, et al. Advances of QTL Localization and GWAS Application in Crop Resistances Against Plant-Parasitic Nematodes. Agronomy. 2025; 15(10):2370. https://doi.org/10.3390/agronomy15102370

Chicago/Turabian Style

Yu, Jing-Wen, Ling-Wei Wan, Huan-Huan Hao, Wen-Cui Wu, Ya-Qin Liu, Xi-Yue Yu, De-Liang Peng, Huan Peng, Shi-Ming Liu, Ling-An Kong, and et al. 2025. "Advances of QTL Localization and GWAS Application in Crop Resistances Against Plant-Parasitic Nematodes" Agronomy 15, no. 10: 2370. https://doi.org/10.3390/agronomy15102370

APA Style

Yu, J.-W., Wan, L.-W., Hao, H.-H., Wu, W.-C., Liu, Y.-Q., Yu, X.-Y., Peng, D.-L., Peng, H., Liu, S.-M., Kong, L.-A., Kang, H.-X., & Huang, W.-K. (2025). Advances of QTL Localization and GWAS Application in Crop Resistances Against Plant-Parasitic Nematodes. Agronomy, 15(10), 2370. https://doi.org/10.3390/agronomy15102370

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